Overview

Dataset statistics

Number of variables16
Number of observations495
Missing cells355
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.0 KiB
Average record size in memory128.3 B

Variable types

DateTime1
Text3
Numeric8
Categorical4

Alerts

Latitude is highly overall correlated with Region and 1 other fieldsHigh correlation
Longitude is highly overall correlated with Region and 1 other fieldsHigh correlation
Month1 is highly overall correlated with MonthHigh correlation
Order is highly overall correlated with Season and 2 other fieldsHigh correlation
Season is highly overall correlated with Order and 1 other fieldsHigh correlation
Year is highly overall correlated with Order and 2 other fieldsHigh correlation
Month is highly overall correlated with Month1 and 1 other fieldsHigh correlation
Region is highly overall correlated with Latitude and 2 other fieldsHigh correlation
Show is highly overall correlated with Order and 2 other fieldsHigh correlation
State is highly overall correlated with Latitude and 2 other fieldsHigh correlation
State has 355 (71.7%) missing valuesMissing
Order is uniformly distributedUniform
Order has unique valuesUnique

Reproduction

Analysis started2023-07-26 00:01:24.479301
Analysis finished2023-07-26 00:01:36.130488
Duration11.65 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

Air_Date
Date

Distinct265
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2002-01-08 00:00:00
Maximum2018-06-24 00:00:00
2023-07-25T19:01:36.254927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-25T19:01:36.446739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

City
Text

Distinct392
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:36.682748image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length34
Median length28
Mean length8.7757576
Min length3

Characters and Unicode

Total characters4344
Distinct characters72
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)64.8%

Sample

1st rowTokyo
2nd rowAtami
3rd rowHo Chi Minh City
4th rowMekong River
5th rowPhnom Penh
ValueCountFrequency (%)
city 15
 
2.2%
new 9
 
1.3%
san 9
 
1.3%
tokyo 5
 
0.7%
singapore 5
 
0.7%
st 5
 
0.7%
york 5
 
0.7%
los 5
 
0.7%
orleans 4
 
0.6%
london 4
 
0.6%
Other values (475) 605
90.2%
2023-07-25T19:01:37.100962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 511
 
11.8%
e 345
 
7.9%
n 340
 
7.8%
o 311
 
7.2%
i 283
 
6.5%
r 238
 
5.5%
t 201
 
4.6%
176
 
4.1%
s 171
 
3.9%
l 168
 
3.9%
Other values (62) 1600
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3466
79.8%
Uppercase Letter 674
 
15.5%
Space Separator 176
 
4.1%
Other Punctuation 15
 
0.3%
Dash Punctuation 12
 
0.3%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 511
14.7%
e 345
10.0%
n 340
9.8%
o 311
9.0%
i 283
 
8.2%
r 238
 
6.9%
t 201
 
5.8%
s 171
 
4.9%
l 168
 
4.8%
u 146
 
4.2%
Other values (30) 752
21.7%
Uppercase Letter
ValueCountFrequency (%)
C 68
 
10.1%
P 66
 
9.8%
M 65
 
9.6%
S 65
 
9.6%
B 55
 
8.2%
A 42
 
6.2%
L 35
 
5.2%
T 30
 
4.5%
H 29
 
4.3%
K 27
 
4.0%
Other values (16) 192
28.5%
Other Punctuation
ValueCountFrequency (%)
, 8
53.3%
. 5
33.3%
' 2
 
13.3%
Space Separator
ValueCountFrequency (%)
176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4140
95.3%
Common 204
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 511
 
12.3%
e 345
 
8.3%
n 340
 
8.2%
o 311
 
7.5%
i 283
 
6.8%
r 238
 
5.7%
t 201
 
4.9%
s 171
 
4.1%
l 168
 
4.1%
u 146
 
3.5%
Other values (56) 1426
34.4%
Common
ValueCountFrequency (%)
176
86.3%
- 12
 
5.9%
, 8
 
3.9%
. 5
 
2.5%
' 2
 
1.0%
’ 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4321
99.5%
None 17
 
0.4%
Latin Ext Additional 5
 
0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 511
 
11.8%
e 345
 
8.0%
n 340
 
7.9%
o 311
 
7.2%
i 283
 
6.5%
r 238
 
5.5%
t 201
 
4.7%
176
 
4.1%
s 171
 
4.0%
l 168
 
3.9%
Other values (47) 1577
36.5%
None
ValueCountFrequency (%)
í 3
17.6%
ó 2
11.8%
â 2
11.8%
è 2
11.8%
á 2
11.8%
é 2
11.8%
ã 2
11.8%
ç 1
 
5.9%
ș 1
 
5.9%
Latin Ext Additional
ValueCountFrequency (%)
ả 1
20.0%
ị 1
20.0%
ế 1
20.0%
ầ 1
20.0%
ộ 1
20.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Country
Text

Distinct86
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:37.323869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length32
Median length19
Mean length9.1919192
Min length4

Characters and Unicode

Total characters4550
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)4.8%

Sample

1st rowJapan
2nd rowJapan
3rd rowVietnam
4th rowVietnam
5th rowCambodia
ValueCountFrequency (%)
united 152
21.6%
states 141
20.0%
france 21
 
3.0%
italy 20
 
2.8%
japan 16
 
2.3%
china 15
 
2.1%
vietnam 13
 
1.8%
mexico 12
 
1.7%
puerto 11
 
1.6%
rico 11
 
1.6%
Other values (92) 292
41.5%
2023-07-25T19:01:37.711017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 559
12.3%
t 544
12.0%
e 466
 
10.2%
i 396
 
8.7%
n 384
 
8.4%
d 216
 
4.7%
209
 
4.6%
s 187
 
4.1%
S 171
 
3.8%
U 165
 
3.6%
Other values (38) 1253
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3650
80.2%
Uppercase Letter 690
 
15.2%
Space Separator 209
 
4.6%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 559
15.3%
t 544
14.9%
e 466
12.8%
i 396
10.8%
n 384
10.5%
d 216
 
5.9%
s 187
 
5.1%
r 142
 
3.9%
o 127
 
3.5%
c 94
 
2.6%
Other values (14) 535
14.7%
Uppercase Letter
ValueCountFrequency (%)
S 171
24.8%
U 165
23.9%
C 55
 
8.0%
P 38
 
5.5%
I 37
 
5.4%
M 25
 
3.6%
F 25
 
3.6%
R 24
 
3.5%
J 19
 
2.8%
G 18
 
2.6%
Other values (12) 113
16.4%
Space Separator
ValueCountFrequency (%)
209
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4340
95.4%
Common 210
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 559
12.9%
t 544
12.5%
e 466
10.7%
i 396
 
9.1%
n 384
 
8.8%
d 216
 
5.0%
s 187
 
4.3%
S 171
 
3.9%
U 165
 
3.8%
r 142
 
3.3%
Other values (36) 1110
25.6%
Common
ValueCountFrequency (%)
209
99.5%
- 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 559
12.3%
t 544
12.0%
e 466
 
10.2%
i 396
 
8.7%
n 384
 
8.4%
d 216
 
4.7%
209
 
4.6%
s 187
 
4.1%
S 171
 
3.8%
U 165
 
3.6%
Other values (38) 1253
27.5%

Day
Real number (ℝ)

Distinct31
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.086869
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:37.873833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.0127288
Coefficient of variation (CV)0.59738896
Kurtosis-1.2462899
Mean15.086869
Median Absolute Deviation (MAD)8
Skewness0.094502353
Sum7468
Variance81.229281
MonotonicityNot monotonic
2023-07-25T19:01:38.021991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 28
 
5.7%
29 26
 
5.3%
8 26
 
5.3%
15 25
 
5.1%
4 24
 
4.8%
5 22
 
4.4%
22 21
 
4.2%
28 20
 
4.0%
7 20
 
4.0%
26 18
 
3.6%
Other values (21) 265
53.5%
ValueCountFrequency (%)
1 28
5.7%
2 12
2.4%
3 13
2.6%
4 24
4.8%
5 22
4.4%
6 6
 
1.2%
7 20
4.0%
8 26
5.3%
9 16
3.2%
10 18
3.6%
ValueCountFrequency (%)
31 5
 
1.0%
30 9
 
1.8%
29 26
5.3%
28 20
4.0%
27 14
2.8%
26 18
3.6%
25 12
2.4%
24 12
2.4%
23 14
2.8%
22 21
4.2%

Episode
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4666667
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:38.192245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q310
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9832107
Coefficient of variation (CV)0.66739429
Kurtosis0.13577258
Mean7.4666667
Median Absolute Deviation (MAD)3
Skewness0.88239028
Sum3696
Variance24.832389
MonotonicityNot monotonic
2023-07-25T19:01:38.339057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6 52
10.5%
2 49
9.9%
3 43
 
8.7%
4 42
 
8.5%
5 41
 
8.3%
7 39
 
7.9%
8 38
 
7.7%
1 29
 
5.9%
9 29
 
5.9%
15 25
 
5.1%
Other values (14) 108
21.8%
ValueCountFrequency (%)
1 29
5.9%
2 49
9.9%
3 43
8.7%
4 42
8.5%
5 41
8.3%
6 52
10.5%
7 39
7.9%
8 38
7.7%
9 29
5.9%
10 17
 
3.4%
ValueCountFrequency (%)
25 1
 
0.2%
24 1
 
0.2%
22 1
 
0.2%
21 2
 
0.4%
20 5
 
1.0%
19 7
 
1.4%
18 5
 
1.0%
17 7
 
1.4%
16 10
 
2.0%
15 25
5.1%

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct400
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.165775
Minimum-77.846388
Maximum64.126521
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)12.5%
Memory size4.0 KiB
2023-07-25T19:01:38.503804image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-77.846388
5-th percentile-33.021672
Q116.830208
median33.328618
Q340.842627
95-th percentile50.334136
Maximum64.126521
Range141.97291
Interquartile range (IQR)24.012419

Descriptive statistics

Standard deviation23.941123
Coefficient of variation (CV)0.95133661
Kurtosis2.5735669
Mean25.165775
Median Absolute Deviation (MAD)11.129873
Skewness-1.5374302
Sum12457.058
Variance573.17736
MonotonicityNot monotonic
2023-07-25T19:01:38.696170image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.689487 5
 
1.0%
1.352083 5
 
1.0%
40.712775 5
 
1.0%
34.052234 4
 
0.8%
21.027764 4
 
0.8%
29.951066 4
 
0.8%
40.78306 4
 
0.8%
51.507351 4
 
0.8%
43.318334 3
 
0.6%
45.501689 3
 
0.6%
Other values (390) 454
91.7%
ValueCountFrequency (%)
-77.846388 1
0.2%
-77.633703 1
0.2%
-77.554281 1
0.2%
-72.294011 1
0.2%
-43.532054 1
0.2%
-41.472233 1
0.2%
-41.31755 1
0.2%
-41.163458 1
0.2%
-37.813628 2
0.4%
-34.905408 1
0.2%
ValueCountFrequency (%)
64.126521 1
 
0.2%
60.169856 1
 
0.2%
59.93428 3
0.6%
59.329323 1
 
0.2%
58.455121 1
 
0.2%
55.953252 1
 
0.2%
55.864237 2
0.4%
55.755826 2
0.4%
55.676097 1
 
0.2%
54.597285 1
 
0.2%

Longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct400
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-15.130647
Minimum-159.46917
Maximum172.63622
Zeros0
Zeros (%)0.0%
Negative282
Negative (%)57.0%
Memory size4.0 KiB
2023-07-25T19:01:38.879352image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-159.46917
5-th percentile-118.24368
Q1-79.41537
median-17.467686
Q335.20293
95-th percentile126.97797
Maximum172.63622
Range332.10539
Interquartile range (IQR)114.6183

Descriptive statistics

Standard deviation80.729155
Coefficient of variation (CV)-5.3354728
Kurtosis-0.91765033
Mean-15.130647
Median Absolute Deviation (MAD)58.979946
Skewness0.45079061
Sum-7489.6702
Variance6517.1965
MonotonicityNot monotonic
2023-07-25T19:01:39.042375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139.691706 5
 
1.0%
103.819836 5
 
1.0%
-74.005973 5
 
1.0%
-118.243685 4
 
0.8%
105.83416 4
 
0.8%
-90.071532 4
 
0.8%
-73.971249 4
 
0.8%
-0.127758 4
 
0.8%
-1.981231 3
 
0.6%
-73.567256 3
 
0.6%
Other values (390) 454
91.7%
ValueCountFrequency (%)
-159.469167 1
0.2%
-158.071598 1
0.2%
-158.056896 1
0.2%
-157.858333 2
0.4%
-157.02263 1
0.2%
-149.558476 1
0.2%
-147.651301 1
0.2%
-139.013569 1
0.2%
-123.120738 1
0.2%
-123.100707 1
0.2%
ValueCountFrequency (%)
172.636225 1
 
0.2%
166.66833 1
 
0.2%
166.16443 1
 
0.2%
162.8805 1
 
0.2%
151.209296 2
 
0.4%
144.963058 2
 
0.4%
141.354376 1
 
0.2%
139.883565 1
 
0.2%
139.691706 5
1.0%
139.071705 1
 
0.2%

Month1
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0282828
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:39.198368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1157183
Coefficient of variation (CV)0.51685005
Kurtosis-1.0625017
Mean6.0282828
Median Absolute Deviation (MAD)2
Skewness0.026818014
Sum2984
Variance9.7077005
MonotonicityNot monotonic
2023-07-25T19:01:39.328139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 64
12.9%
8 59
11.9%
4 58
11.7%
1 51
10.3%
10 50
10.1%
7 44
8.9%
3 38
7.7%
6 33
6.7%
9 32
6.5%
11 31
6.3%
Other values (2) 35
7.1%
ValueCountFrequency (%)
1 51
10.3%
2 26
5.3%
3 38
7.7%
4 58
11.7%
5 64
12.9%
6 33
6.7%
7 44
8.9%
8 59
11.9%
9 32
6.5%
10 50
10.1%
ValueCountFrequency (%)
12 9
 
1.8%
11 31
6.3%
10 50
10.1%
9 32
6.5%
8 59
11.9%
7 44
8.9%
6 33
6.7%
5 64
12.9%
4 58
11.7%
3 38
7.7%

Month
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
May
64 
August
59 
April
58 
January
51 
October
50 
Other values (7)
213 

Length

Max length9
Median length7
Mean length5.7717172
Min length3

Characters and Unicode

Total characters2857
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowJanuary

Common Values

ValueCountFrequency (%)
May 64
12.9%
August 59
11.9%
April 58
11.7%
January 51
10.3%
October 50
10.1%
July 44
8.9%
March 38
7.7%
June 33
6.7%
September 32
6.5%
November 31
6.3%
Other values (2) 35
7.1%

Length

2023-07-25T19:01:39.508267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 64
12.9%
august 59
11.9%
april 58
11.7%
january 51
10.3%
october 50
10.1%
july 44
8.9%
march 38
7.7%
june 33
6.7%
september 32
6.5%
november 31
6.3%
Other values (2) 35
7.1%

Most occurring characters

ValueCountFrequency (%)
r 321
 
11.2%
e 294
 
10.3%
u 272
 
9.5%
a 230
 
8.1%
y 185
 
6.5%
b 148
 
5.2%
t 141
 
4.9%
J 128
 
4.5%
A 117
 
4.1%
M 102
 
3.6%
Other values (16) 919
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2362
82.7%
Uppercase Letter 495
 
17.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 321
13.6%
e 294
12.4%
u 272
11.5%
a 230
9.7%
y 185
 
7.8%
b 148
 
6.3%
t 141
 
6.0%
l 102
 
4.3%
c 97
 
4.1%
p 90
 
3.8%
Other values (8) 482
20.4%
Uppercase Letter
ValueCountFrequency (%)
J 128
25.9%
A 117
23.6%
M 102
20.6%
O 50
 
10.1%
S 32
 
6.5%
N 31
 
6.3%
F 26
 
5.3%
D 9
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2857
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 321
 
11.2%
e 294
 
10.3%
u 272
 
9.5%
a 230
 
8.1%
y 185
 
6.5%
b 148
 
5.2%
t 141
 
4.9%
J 128
 
4.5%
A 117
 
4.1%
M 102
 
3.6%
Other values (16) 919
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2857
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 321
 
11.2%
e 294
 
10.3%
u 272
 
9.5%
a 230
 
8.1%
y 185
 
6.5%
b 148
 
5.2%
t 141
 
4.9%
J 128
 
4.5%
A 117
 
4.1%
M 102
 
3.6%
Other values (16) 919
32.2%

Order
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct495
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.62222
Minimum1
Maximum496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:39.674728image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.7
Q1124.5
median249
Q3372.5
95-th percentile471.3
Maximum496
Range495
Interquartile range (IQR)248

Descriptive statistics

Standard deviation143.44625
Coefficient of variation (CV)0.57696472
Kurtosis-1.2024171
Mean248.62222
Median Absolute Deviation (MAD)124
Skewness-0.0024140102
Sum123068
Variance20576.827
MonotonicityNot monotonic
2023-07-25T19:01:39.856717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
342 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
335 1
 
0.2%
334 1
 
0.2%
333 1
 
0.2%
Other values (485) 485
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%
490 1
0.2%
489 1
0.2%
488 1
0.2%
487 1
0.2%

Region
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
North America
159 
Asia
94 
Europe
94 
South America
42 
Africa
32 
Other values (4)
74 

Length

Max length15
Median length13
Mean length9.3474747
Min length4

Characters and Unicode

Total characters4627
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowAsia
3rd rowAsia
4th rowAsia
5th rowAsia

Common Values

ValueCountFrequency (%)
North America 159
32.1%
Asia 94
19.0%
Europe 94
19.0%
South America 42
 
8.5%
Africa 32
 
6.5%
Central America 32
 
6.5%
Middle East 24
 
4.8%
Oceania 14
 
2.8%
Antarctica 4
 
0.8%

Length

2023-07-25T19:01:40.020482image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-25T19:01:40.189954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
america 233
31.0%
north 159
21.1%
asia 94
12.5%
europe 94
12.5%
south 42
 
5.6%
africa 32
 
4.3%
central 32
 
4.3%
middle 24
 
3.2%
east 24
 
3.2%
oceania 14
 
1.9%

Most occurring characters

ValueCountFrequency (%)
r 554
12.0%
a 451
9.7%
i 401
 
8.7%
e 397
 
8.6%
A 363
 
7.8%
o 295
 
6.4%
c 287
 
6.2%
t 265
 
5.7%
257
 
5.6%
m 233
 
5.0%
Other values (14) 1124
24.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3618
78.2%
Uppercase Letter 752
 
16.3%
Space Separator 257
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 554
15.3%
a 451
12.5%
i 401
11.1%
e 397
11.0%
o 295
8.2%
c 287
7.9%
t 265
7.3%
m 233
6.4%
h 201
 
5.6%
u 136
 
3.8%
Other values (6) 398
11.0%
Uppercase Letter
ValueCountFrequency (%)
A 363
48.3%
N 159
21.1%
E 118
 
15.7%
S 42
 
5.6%
C 32
 
4.3%
M 24
 
3.2%
O 14
 
1.9%
Space Separator
ValueCountFrequency (%)
257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4370
94.4%
Common 257
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 554
12.7%
a 451
10.3%
i 401
9.2%
e 397
9.1%
A 363
 
8.3%
o 295
 
6.8%
c 287
 
6.6%
t 265
 
6.1%
m 233
 
5.3%
h 201
 
4.6%
Other values (13) 923
21.1%
Common
ValueCountFrequency (%)
257
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 554
12.0%
a 451
9.7%
i 401
 
8.7%
e 397
 
8.6%
A 363
 
7.8%
o 295
 
6.4%
c 287
 
6.2%
t 265
 
5.7%
257
 
5.6%
m 233
 
5.0%
Other values (14) 1124
24.3%

Season
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6505051
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:40.343028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9587369
Coefficient of variation (CV)0.63621841
Kurtosis-0.80999977
Mean4.6505051
Median Absolute Deviation (MAD)2
Skewness0.51997948
Sum2302
Variance8.7541242
MonotonicityNot monotonic
2023-07-25T19:01:40.470771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 83
16.8%
1 79
16.0%
4 62
12.5%
5 48
9.7%
3 44
8.9%
6 42
8.5%
7 39
7.9%
9 31
 
6.3%
8 29
 
5.9%
11 21
 
4.2%
ValueCountFrequency (%)
1 79
16.0%
2 83
16.8%
3 44
8.9%
4 62
12.5%
5 48
9.7%
6 42
8.5%
7 39
7.9%
8 29
 
5.9%
9 31
 
6.3%
10 17
 
3.4%
ValueCountFrequency (%)
11 21
 
4.2%
10 17
 
3.4%
9 31
 
6.3%
8 29
 
5.9%
7 39
7.9%
6 42
8.5%
5 48
9.7%
4 62
12.5%
3 44
8.9%
2 83
16.8%

Show
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
No Reservations
275 
Parts Unknown
158 
A Cook's Tour
42 
The Layover
 
20

Length

Max length15
Median length15
Mean length14.030303
Min length11

Characters and Unicode

Total characters6945
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA Cook's Tour
2nd rowA Cook's Tour
3rd rowA Cook's Tour
4th rowA Cook's Tour
5th rowA Cook's Tour

Common Values

ValueCountFrequency (%)
No Reservations 275
55.6%
Parts Unknown 158
31.9%
A Cook's Tour 42
 
8.5%
The Layover 20
 
4.0%

Length

2023-07-25T19:01:40.651375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-25T19:01:40.846080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
no 275
26.6%
reservations 275
26.6%
parts 158
15.3%
unknown 158
15.3%
a 42
 
4.1%
cook's 42
 
4.1%
tour 42
 
4.1%
the 20
 
1.9%
layover 20
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o 854
12.3%
s 750
10.8%
n 749
10.8%
e 590
 
8.5%
537
 
7.7%
r 495
 
7.1%
a 453
 
6.5%
t 433
 
6.2%
v 295
 
4.2%
N 275
 
4.0%
Other values (14) 1514
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5334
76.8%
Uppercase Letter 1032
 
14.9%
Space Separator 537
 
7.7%
Other Punctuation 42
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 854
16.0%
s 750
14.1%
n 749
14.0%
e 590
11.1%
r 495
9.3%
a 453
8.5%
t 433
8.1%
v 295
 
5.5%
i 275
 
5.2%
k 200
 
3.7%
Other values (4) 240
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
N 275
26.6%
R 275
26.6%
P 158
15.3%
U 158
15.3%
T 62
 
6.0%
A 42
 
4.1%
C 42
 
4.1%
L 20
 
1.9%
Space Separator
ValueCountFrequency (%)
537
100.0%
Other Punctuation
ValueCountFrequency (%)
' 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6366
91.7%
Common 579
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 854
13.4%
s 750
11.8%
n 749
11.8%
e 590
9.3%
r 495
7.8%
a 453
 
7.1%
t 433
 
6.8%
v 295
 
4.6%
N 275
 
4.3%
R 275
 
4.3%
Other values (12) 1197
18.8%
Common
ValueCountFrequency (%)
537
92.7%
' 42
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6945
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 854
12.3%
s 750
10.8%
n 749
10.8%
e 590
 
8.5%
537
 
7.7%
r 495
 
7.1%
a 453
 
6.5%
t 433
 
6.2%
v 295
 
4.2%
N 275
 
4.0%
Other values (14) 1514
21.8%

State
Categorical

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)24.3%
Missing355
Missing (%)71.7%
Memory size4.0 KiB
California
22 
New York
18 
Texas
11 
New Jersey
11 
Louisiana
10 
Other values (29)
68 

Length

Max length14
Median length12.5
Mean length8.7142857
Min length4

Characters and Unicode

Total characters1220
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)7.1%

Sample

1st rowCalifornia
2nd rowCalifornia
3rd rowCalifornia
4th rowNew York
5th rowNew York

Common Values

ValueCountFrequency (%)
California 22
 
4.4%
New York 18
 
3.6%
Texas 11
 
2.2%
New Jersey 11
 
2.2%
Louisiana 10
 
2.0%
Hawaii 6
 
1.2%
West Virginia 5
 
1.0%
New Mexico 5
 
1.0%
Montana 4
 
0.8%
Massachusetts 4
 
0.8%
Other values (24) 44
 
8.9%
(Missing) 355
71.7%

Length

2023-07-25T19:01:41.001985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 34
18.6%
california 22
 
12.0%
york 18
 
9.8%
texas 11
 
6.0%
jersey 11
 
6.0%
louisiana 10
 
5.5%
hawaii 6
 
3.3%
virginia 6
 
3.3%
west 5
 
2.7%
mexico 5
 
2.7%
Other values (26) 55
30.1%

Most occurring characters

ValueCountFrequency (%)
a 150
12.3%
i 147
 
12.0%
e 96
 
7.9%
o 92
 
7.5%
n 87
 
7.1%
s 83
 
6.8%
r 74
 
6.1%
43
 
3.5%
w 40
 
3.3%
l 38
 
3.1%
Other values (33) 370
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 991
81.2%
Uppercase Letter 184
 
15.1%
Space Separator 43
 
3.5%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 150
15.1%
i 147
14.8%
e 96
9.7%
o 92
9.3%
n 87
8.8%
s 83
8.4%
r 74
7.5%
w 40
 
4.0%
l 38
 
3.8%
t 29
 
2.9%
Other values (12) 155
15.6%
Uppercase Letter
ValueCountFrequency (%)
N 37
20.1%
C 29
15.8%
M 25
13.6%
Y 18
9.8%
T 12
 
6.5%
J 11
 
6.0%
W 10
 
5.4%
L 10
 
5.4%
H 6
 
3.3%
V 6
 
3.3%
Other values (9) 20
10.9%
Space Separator
ValueCountFrequency (%)
43
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1175
96.3%
Common 45
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 150
12.8%
i 147
12.5%
e 96
 
8.2%
o 92
 
7.8%
n 87
 
7.4%
s 83
 
7.1%
r 74
 
6.3%
w 40
 
3.4%
l 38
 
3.2%
N 37
 
3.1%
Other values (31) 331
28.2%
Common
ValueCountFrequency (%)
43
95.6%
. 2
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 150
12.3%
i 147
 
12.0%
e 96
 
7.9%
o 92
 
7.5%
n 87
 
7.1%
s 83
 
6.8%
r 74
 
6.1%
43
 
3.5%
w 40
 
3.3%
l 38
 
3.1%
Other values (33) 370
30.3%

Title
Text

Distinct235
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:41.232411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length46
Median length36
Mean length12.058586
Min length3

Characters and Unicode

Total characters5969
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)26.3%

Sample

1st rowA Taste of Tokyo
2nd rowDining with Geishas
3rd rowCobra Heart - Foods That Make You Manly
4th rowEating on the Mekong
5th rowWild Delicacies
ValueCountFrequency (%)
the 27
 
2.8%
new 24
 
2.5%
japan 14
 
1.5%
u.s 12
 
1.2%
southwest 12
 
1.2%
puerto 11
 
1.1%
rico 11
 
1.1%
jersey 11
 
1.1%
of 11
 
1.1%
island 11
 
1.1%
Other values (363) 818
85.0%
2023-07-25T19:01:41.680039image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 599
 
10.0%
467
 
7.8%
e 455
 
7.6%
i 402
 
6.7%
n 390
 
6.5%
o 362
 
6.1%
r 302
 
5.1%
t 248
 
4.2%
s 224
 
3.8%
l 188
 
3.1%
Other values (54) 2332
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4416
74.0%
Uppercase Letter 956
 
16.0%
Space Separator 467
 
7.8%
Other Punctuation 107
 
1.8%
Close Punctuation 7
 
0.1%
Open Punctuation 7
 
0.1%
Dash Punctuation 6
 
0.1%
Decimal Number 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 599
13.6%
e 455
10.3%
i 402
 
9.1%
n 390
 
8.8%
o 362
 
8.2%
r 302
 
6.8%
t 248
 
5.6%
s 224
 
5.1%
l 188
 
4.3%
u 184
 
4.2%
Other values (17) 1062
24.0%
Uppercase Letter
ValueCountFrequency (%)
S 119
 
12.4%
C 86
 
9.0%
B 67
 
7.0%
M 64
 
6.7%
N 52
 
5.4%
P 47
 
4.9%
T 46
 
4.8%
A 45
 
4.7%
U 45
 
4.7%
I 41
 
4.3%
Other values (15) 344
36.0%
Other Punctuation
ValueCountFrequency (%)
. 29
27.1%
, 29
27.1%
: 28
26.2%
/ 11
 
10.3%
& 5
 
4.7%
' 5
 
4.7%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
467
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5372
90.0%
Common 597
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 599
 
11.2%
e 455
 
8.5%
i 402
 
7.5%
n 390
 
7.3%
o 362
 
6.7%
r 302
 
5.6%
t 248
 
4.6%
s 224
 
4.2%
l 188
 
3.5%
u 184
 
3.4%
Other values (42) 2018
37.6%
Common
ValueCountFrequency (%)
467
78.2%
. 29
 
4.9%
, 29
 
4.9%
: 28
 
4.7%
/ 11
 
1.8%
) 7
 
1.2%
( 7
 
1.2%
- 6
 
1.0%
& 5
 
0.8%
' 5
 
0.8%
Other values (2) 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5968
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 599
 
10.0%
467
 
7.8%
e 455
 
7.6%
i 402
 
6.7%
n 390
 
6.5%
o 362
 
6.1%
r 302
 
5.1%
t 248
 
4.2%
s 224
 
3.8%
l 188
 
3.2%
Other values (53) 2331
39.1%
None
ValueCountFrequency (%)
ã 1
100.0%

Year
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.3131
Minimum2002
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-07-25T19:01:41.851031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2002
Q12007
median2010
Q32013
95-th percentile2017
Maximum2018
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.3179469
Coefficient of variation (CV)0.0021478977
Kurtosis-0.79035628
Mean2010.3131
Median Absolute Deviation (MAD)3
Skewness-0.06736882
Sum995105
Variance18.644665
MonotonicityIncreasing
2023-07-25T19:01:41.992079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2008 48
 
9.7%
2012 45
 
9.1%
2013 43
 
8.7%
2006 41
 
8.3%
2011 36
 
7.3%
2009 35
 
7.1%
2007 32
 
6.5%
2010 32
 
6.5%
2017 29
 
5.9%
2002 26
 
5.3%
Other values (6) 128
25.9%
ValueCountFrequency (%)
2002 26
5.3%
2003 16
 
3.2%
2005 21
4.2%
2006 41
8.3%
2007 32
6.5%
2008 48
9.7%
2009 35
7.1%
2010 32
6.5%
2011 36
7.3%
2012 45
9.1%
ValueCountFrequency (%)
2018 21
4.2%
2017 29
5.9%
2016 21
4.2%
2015 23
4.6%
2014 26
5.3%
2013 43
8.7%
2012 45
9.1%
2011 36
7.3%
2010 32
6.5%
2009 35
7.1%

Interactions

2023-07-25T19:01:33.739424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-25T19:01:32.584096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-25T19:01:33.578641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-25T19:01:42.143601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
DayEpisodeLatitudeLongitudeMonth1OrderSeasonYearMonthRegionShowState
Day1.0000.0060.0070.060-0.0880.027-0.0330.0240.1440.1890.0000.422
Episode0.0061.000-0.020-0.1070.240-0.210-0.007-0.2490.3500.1800.2550.377
Latitude0.007-0.0201.000-0.1290.0300.0370.0370.0350.2010.5720.0720.722
Longitude0.060-0.107-0.1291.0000.0300.0040.0270.0030.1690.6500.0990.875
Month1-0.0880.2400.0300.0301.0000.3950.2220.3430.9980.2460.4800.432
Order0.027-0.2100.0370.0040.3951.0000.6340.9970.3710.1700.7870.400
Season-0.033-0.0070.0370.0270.2220.6341.0000.6360.3020.1790.3780.419
Year0.024-0.2490.0350.0030.3430.9970.6361.0000.3730.1260.7930.416
Month0.1440.3500.2010.1690.9980.3710.3020.3731.0000.2490.5390.441
Region0.1890.1800.5720.6500.2460.1700.1790.1260.2491.0000.0950.876
Show0.0000.2550.0720.0990.4800.7870.3780.7930.5390.0951.0000.130
State0.4220.3770.7220.8750.4320.4000.4190.4160.4410.8760.1301.000

Missing values

2023-07-25T19:01:35.683928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-25T19:01:36.000263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Air_DateCityCountryDayEpisodeLatitudeLongitudeMonth1MonthOrderRegionSeasonShowStateTitleYear
02002-01-08TokyoJapan8135.689487139.6917061January1Asia1A Cook's TourNaNA Taste of Tokyo2002
12002-01-12AtamiJapan12235.096276139.0717051January2Asia1A Cook's TourNaNDining with Geishas2002
22002-01-15Ho Chi Minh CityVietnam15310.823099106.6296641January3Asia1A Cook's TourNaNCobra Heart - Foods That Make You Manly2002
32002-01-15Mekong RiverVietnam15415.933589103.4492841January4Asia1A Cook's TourNaNEating on the Mekong2002
42002-01-22Phnom PenhCambodia22511.556374104.9282101January5Asia1A Cook's TourNaNWild Delicacies2002
52002-01-29BattambangCambodia29613.095730103.2022051January6Asia1A Cook's TourNaNEating on the Edge of Nowhere2002
62002-01-29PailinCambodia29612.909296102.6675571January7Asia1A Cook's TourNaNEating on the Edge of Nowhere2002
72002-01-29TokyoJapan29635.689487139.6917061January8Asia1A Cook's TourNaNEating on the Edge of Nowhere2002
82002-02-05PortoPortugal5741.157944-8.6291052February9Europe1A Cook's TourNaNCod Crazy2002
92002-02-12San SebastianSpain12843.318334-1.9812312February10Europe1A Cook's TourNaNSan Sebastian: A Food Lover's Town2002
Air_DateCityCountryDayEpisodeLatitudeLongitudeMonth1MonthOrderRegionSeasonShowStateTitleYear
4852018-05-20YerevanArmenia20440.17918644.4991035May487Asia11Parts UnknownNaNArmenia2018
4862018-06-03Hong KongChina3522.396428114.1094976June488Asia11Parts UnknownNaNHong Kong2018
4872018-06-10BerlinGermany10652.52000713.4049546June489Europe11Parts UnknownNaNBerlin2018
4882018-06-17MamouUnited States17730.633809-92.4192996June490North America11Parts UnknownLouisianaCajun Mardi Gras2018
4892018-06-17Grand CoteauUnited States17730.419920-92.0465096June491North America11Parts UnknownLouisianaCajun Mardi Gras2018
4902018-06-17LafayetteUnited States17730.224090-92.0198436June492North America11Parts UnknownLouisianaCajun Mardi Gras2018
4912018-06-17OpelousasUnited States17730.533530-92.0815096June493North America11Parts UnknownLouisianaCajun Mardi Gras2018
4922018-06-24BumthangBhutan24827.64183990.6773056June494Asia11Parts UnknownNaNBhutan2018
4932018-06-24PunakhaBhutan24827.59208789.8797466June495Asia11Parts UnknownNaNBhutan2018
4942018-06-24ThimphuBhutan24827.47279289.6392866June496Asia11Parts UnknownNaNBhutan2018